Consolidation: a method for reasoning about the behavior of devices
Thomas Clare Bylander · OhioLink ETD Center (Ohio Library and Information Network) · 1986
Research on Naive Physics attempts to answer the questions: How do people reason about physical phenomena? How can computers be endowed with similar facilities? Artificial Intelligence research on Naive Physics concentrates on the second question, and by doing so, also seeks to achieve significant insight on the first. This research addresses one problem of Naive Physics, that of deriving the of a device given the structure of the device and the potential behavior of its parts. The potential behavior of a physical object describes the object's behavioral characteristics without making assumptions about the behavior of other objects external to that object. The reasoning process that this research proposes is based on two strategies. The consolidation strategy is to select a component consisting of two components and infer the potential behavior of the composite from the potential behavior of its subcomponents. Successful application of consolidation on increasingly larger composite components results in inferring the potential behavior of the whole device. The other strategy is to represent potential behavior with a small number of of that behavioral interactions to be described by rules of composition. A type of behavior is an action on a substance at some location or on some path. The rules of composition, called patterns, describe how one type of behavior can arise from a structural combination of other types of behavior. For example, the causal pattern states that a move behavior can arise from an allow behavior and a pump behavior if both behaviors are on the same path and if the path goes from a potential source to a potential sink. These two strategies are incorporated into an overall framework for representing simple devices and reasoning about their potential behavior. In addition to introducing the consolidation framework and presenting examples of applying it, this dissertation also discusses the kinds of Artificial Intelligence theories that are appropriate for Naive Physics, carefully compares consolidation with qualitative simulation, and lists several areas for future research, including suggestions on how to overcome shortcomings of the proposed consolidation framework.